A Probabilistic Model for Diversifying Recommendation Lists

  • Yutaka Kabutoya
  • Tomoharu Iwata
  • Hiroyuki Toda
  • Hiroyuki Kitagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


We propose a probabilistic method to diversify the results of collaborative filtering. Recommendation diversity is being studied by many researchers as a critical factor that significantly influences user satisfaction. Unlike conventional approaches to recommendation diversification, we theoretically derived a diversification method. Specifically, our method naturally diversifies a recommendation list by maximizing the probability that a user selects at most one item from the list. For enhanced practicality, we formulate a model for the proposed method on three policies — robust estimation, the use of only purchase history, and the elimination of any hyperparameters controlling the diversity. In this paper, we formally demonstrate that our method is practically superior to conventional diversification methods, and experimentally show that our method is competitive with conventional methods in terms of accuracy and diversity.


Latent Dirichlet Allocation Original List Recommendation List Recommendation Accuracy Recommended Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adomavicious, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE TKDE 17(6), 734–749 (2005)Google Scholar
  2. 2.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  3. 3.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998, pp. 335–336 (1998)Google Scholar
  4. 4.
    Chen, H., Karger, D.: Less is more: probabilistic models for retrieving fewer relevant documents. In: SIGIR 2006, pp. 429–436 (2006)Google Scholar
  5. 5.
    Clarke, C., Kolla, M., Cormack, G., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: SIGIR 2008, pp. 659–666 (2008)Google Scholar
  6. 6.
    Davidson, J., Liebald, B., Liu, J., Nandy, P., Vleet, T.V.: The YouTube video recommendation system. In: RecSys 2010, pp. 293–296 (2010)Google Scholar
  7. 7.
    Griffiths, T., Steyvers, M.: Finding scientific topics. PNAS 101, 5228–5235 (2004)CrossRefGoogle Scholar
  8. 8.
    Guo, S., Sanner, S.: Probabilistic latent maximal marginal relevance. In: SIGIR 2010, pp. 833–834 (2010)Google Scholar
  9. 9.
    Hurley, N., Zhang, M.: Novelty and diversity in top-N recommendation – analysis and evaluation. ACM Trans. Internet Technol. 10(4), 14:1–14:30 (2011)Google Scholar
  10. 10.
    Koren, Y.: Collaborative filtering with temporal dynamics. In: KDD 2009, pp. 447–456 (2009)Google Scholar
  11. 11.
    Lathia, N., Hailes, S., Capra, L., Amatriain, X.: Temporal diversity in recommender systems. In: SIGIR 2010, pp. 210–217 (2010)Google Scholar
  12. 12.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)CrossRefGoogle Scholar
  13. 13.
    Minka, T.: Estimating a Dirichlet distribution. Tech. rep., M. I. T. (2000)Google Scholar
  14. 14.
    Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: SIGIR 2006, pp. 691–692 (2006)Google Scholar
  15. 15.
    Santos, R., Macdonald, C., Ounis, I.: Selectively diversifying web search results. In: CIKM 2010, pp. 1179–1188 (2010)Google Scholar
  16. 16.
    Santos, R., Macdonald, C., Ounis, I.: Intent-aware search result diversification. In: SIGIR 2011, pp. 595–604 (2011)Google Scholar
  17. 17.
    Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: SIGIR 2012, pp. 175–184 (2012)Google Scholar
  18. 18.
    Tösher, A., Jahrer, M., Bell, R.: The BigChaos solution to the Netflix grand prize (2009),
  19. 19.
    Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: RecSys 2011, pp. 109–116 (2011)Google Scholar
  20. 20.
    Yang, Y., Liu, X.: A re-examination of text categorization methods. In: SIGIR 1999, pp. 42–49 (1999)Google Scholar
  21. 21.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW 2005, pp. 22–32 (2005)Google Scholar
  22. 22.
    Ziegler, C., Lausen, G., Schmidt-Thieme, L.: Taxonomy-driven computation of product recommendations. In: CIKM 2004, pp. 406–415 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yutaka Kabutoya
    • 1
  • Tomoharu Iwata
    • 2
  • Hiroyuki Toda
    • 1
  • Hiroyuki Kitagawa
    • 3
  1. 1.NTT Service Evolution LaboratoriesNTT CorporationJapan
  2. 2.NTT Communication Science LaboratoriesNTT CorporationJapan
  3. 3.Faculty of Engineering, Information and SystemsUniversity of TsukubaJapan

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